
Expanding its presence
Mobile Embrace Ltd has announced the US expansion of its m-payments business, Convey, with the launch on US mobile carrier Sprint.
Mobile Embrace is launching mobile payments on the US Sprint network enabling easy on mobile payment options for Sprint customers to consume digital products and services aggregated by Convey, and serving mobile consumers increasing demand for digital content “here and now”. The US Sprint network has approximately 53 million mobile customers.
Customer Acquisition will be facilitated by the Company’s Media Trading Desk that can buy and manage mobile advertising globally in real time, all from the Company’s base in Sydney. The current mobile phone subscriber market in Australia is approximately 23 million.
Customers will be managed via the Company’s proprietary Customer Management Platform that manages mobile billing relationships and product performance.
The technical integration with Sprint is complete and will now move into ‘test and learn’ phase prior to next calendar years anticipated full roll out.
Mobile Embrace’s m-payments business is now delivering growing revenues and profits in a global market forecast to grow 500% to 2017.
Whitepapers
Related reading
Central banks best suited to issue digital currencies
By Aaran Fronda A recent report by the Official Monetary and Financial Institutions Forum (OMFIF) said that central banks rather than private ... read more
Instant payments: innovations inbound for corporates
In 2020, instant payments look set to continue their current trajectory to become the biggest trend in payments. While these schemes already offer numerous benefits to corporates, leveraging innovations such as APIs and request to pay will go some way to unlocking their full potential, argues Michael Knetsch
Obstacles exist for banks to meet ECB’s instant payments goal
The cost of joining instant payment platforms will be one of many hurdles banks and payment services providers must overcome to meet ... read more
Banks must be aware of “biases” in data used to train ML models
Financial institutions need to be conscious of biases in the historical data that is being used to train machine learning (ML) models, ... read more